Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

TL;DR

This paper introduces coordinated bidding algorithms for repeated second-price auctions with budgets, improving client utilities through strategic cooperation and analyzing incentives and welfare outcomes.

Contribution

It presents the first algorithms for coordinated bidding in online repeated auctions with budget constraints, ensuring higher client utilities and maximal coalition welfare.

Findings

Algorithms guarantee higher utility for each client compared to independent bidding.

Proven maximal coalition welfare in coordinated bidding scenarios.

Experimental validation on synthetic and real data supports effectiveness.

Abstract

In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal coalition welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.